In the age of data overflow and data overload, users of raw data generated, received, processed, etc., from devices constantly use computing devices to analyze these data to find meanings therein or identify meanings that may have overlooked before. With the tremendous growth of cloud storage and computing, data processing or hosting providers continue to increase data storage capacities for the users. At the same, with the increased processing power of processors or microprocessors, as well as internet access speed, the gap between a client-based data processing and cloud-based data processing has decreased dramatically.
The focus on constant increase in data storage and computing power appears, among other things, to address an issue that have negatively affected the table-record data organization structure scheme and data structure software programming. That issue relates to the amount of time, as a function of data organization and/or structure, it takes to obtain the desired data result from queries of datasets. The increase in computing power and data storage technology (e.g., from hard drive disks (HDD) to solid state drives (SSD)) attempts to lessen or alleviate the impact of searching, accessing, and processing of data. However, the time factor is more pronounced especially when the datasets needed for processing includes a very large set, such as a dataset with millions or billions of records.